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Chen Y, Shen TJ. Minimum habitat size required to detect new rare species. Ecology 2024:e4400. [PMID: 39251195 DOI: 10.1002/ecy.4400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 03/21/2024] [Accepted: 05/20/2024] [Indexed: 09/11/2024]
Abstract
Conservation of species requires the protection of the associated suitable habitat. However, it is usually not known how much habitat is required to detect a single rare species. This problem is important, and it is related directly to the success and optimization of conservation planning. However, to date, no statistical methods have been developed to address this problem adequately. In this study, from a statistical sampling theory, we propose an estimator to estimate the minimum area required to conserve one or more additional new rare species. The estimator is highly accurate, as demonstrated by numerical tests. Applying the estimator in a tropical forest plot showed that the additional habitat size required for discovering an additional individual of a previously unseen tropical tree species is about 3.86 ha with a SE of 1.10 ha. In conclusion, the proposed estimator may be applied to conservation planning by assisting conservation biologists and policymakers to balance urban-related and conservation-related land uses by estimating the minimum detection areas required for species.
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Affiliation(s)
- Youhua Chen
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
| | - Tsung-Jen Shen
- Graduate Institute of Statistics, Department of Applied Mathematics, National Chung Hsing University, Taichung, Taiwan
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Hou S, Yang R, Zhao Z, Cao Y, Tseng TH, Wang F, Wang H, Wang P, Wang X, Yu L. A cost-effective approach to identify conservation priority for 30 × 30 biodiversity target on the premise of food security. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 941:172870. [PMID: 38782279 DOI: 10.1016/j.scitotenv.2024.172870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 04/03/2024] [Accepted: 04/27/2024] [Indexed: 05/25/2024]
Abstract
There is a growing consensus on expanding protected and conserved areas for biodiversity conservation. Nevertheless, it remains uncertain where to expand conserved areas as well as what appropriate management modalities to choose. Moreover, conserved areas expansion should be balanced with crop-related food security challenges. We developed a framework to identify cost-effective areas for expanding protected areas and other effective area-based conservation measures (OECMs), and applied it to China. By combining templates for biodiversity conservation priorities at global scale and the priority conservation areas based on 2413 vertebrates' extinction risk in China, we identified areas with high biodiversity conservation value. We then categorized the priority areas according to human impact, indicating the potential cost of management. As a result of combining the two aspects above, we identified the most cost-effective areas for expanding protected areas and OECMs while excluding both the current and predicted croplands that can be used for food security. The results show that China could expand its protected areas to 22.81 % of the country's land area and establish OECMs in areas accounting for 9.82 % and 17.37 % of the country's land area in a cost-effective approach in two scenarios. In the ambitious scenario, protected and conserved areas would account for a maximum of 40.18 % of terrestrial area, with an average 62.67 % coverage of the 2413 species' suitable habitat. To achieve the goals of protected and conserved areas in Kunming-Montreal Global Biodiversity Framework, countries could apply this framework to identify their protected areas and OECM expansion priorities.
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Affiliation(s)
- Shuyu Hou
- Institute for National Parks, Tsinghua University, Beijing 100084, China; College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou 510642, China
| | - Rui Yang
- Institute for National Parks, Tsinghua University, Beijing 100084, China; Department of Landscape Architecture, School of Architecture, Tsinghua University, Beijing 100084, China.
| | - Zhicong Zhao
- Institute for National Parks, Tsinghua University, Beijing 100084, China; Department of Landscape Architecture, School of Architecture, Tsinghua University, Beijing 100084, China
| | - Yue Cao
- Institute for National Parks, Tsinghua University, Beijing 100084, China; Department of Landscape Architecture, School of Architecture, Tsinghua University, Beijing 100084, China
| | - Tz-Hsuan Tseng
- Institute for National Parks, Tsinghua University, Beijing 100084, China; Department of Landscape Architecture, School of Architecture, Tsinghua University, Beijing 100084, China
| | - Fangyi Wang
- Institute for National Parks, Tsinghua University, Beijing 100084, China; Department of Landscape Architecture, School of Architecture, Tsinghua University, Beijing 100084, China
| | - Hao Wang
- Institute for National Parks, Tsinghua University, Beijing 100084, China; Department of Landscape Architecture, School of Architecture, Tsinghua University, Beijing 100084, China
| | - Pei Wang
- Institute for National Parks, Tsinghua University, Beijing 100084, China; Department of Landscape Architecture, School of Architecture, Tsinghua University, Beijing 100084, China
| | - Xiaoshan Wang
- Institute for National Parks, Tsinghua University, Beijing 100084, China; Department of Landscape Architecture, School of Architecture, Tsinghua University, Beijing 100084, China
| | - Le Yu
- Institute for National Parks, Tsinghua University, Beijing 100084, China; Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China; Ministry of Education Ecological Field Station for East Asian Migratory Birds, Department of Earth System Science, Tsinghua University, Beijing 100084, China
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Zhang H, Shi Z, Feng B, Liu Y, Tang Z, Dong X, Gu X, Qi D, Xu W, Zhou C, Zhang J. Facilitating giant panda crossings of national highway in Wolong area of Giant Panda National Park amid human activities. Ecol Evol 2024; 14:e70067. [PMID: 39076614 PMCID: PMC11286302 DOI: 10.1002/ece3.70067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 07/02/2024] [Accepted: 07/10/2024] [Indexed: 07/31/2024] Open
Abstract
As human activities continue to expand, wildlife persistence faces escalating threats from roads. In Wolong area of Giant Panda National Park, the local giant pandas (Ailuropoda melanoleuca) are divided into two population groups along the National Highway G350 (NHG). Therefore, selecting suitable areas to help those giant pandas communicate across the NHG is necessary. In this research, we evaluated the presence of human activities and simulated their absence to analyze how they affect the giant panda's habitat in Wolong. Subsequently, based on the kernel density estimation (KDE) for giant pandas and the main human distribution locations, we selected suitable areas for the population link between the two road sections on the NHG. We simulated the absence of human activities on the two road sections to compare changes in the habitat suitability index (HSI) and connectivity value (CV) relative to their presence. We aimed to carefully select the area for future giant panda corridor plans and simulate whether eliminating human activities will significantly improve the HSI and CV of the area. Our results show that: (1) Human activities presence has led to subtle changes in the landscape pattern of suitable habitats and a decrease in Wolong by 78.76 km2 compared to their absence. (2) Human activities presence significantly reduced HSI and CV in the 1000 m buffer along the NHG compared to their absence. (3) The HSI and CV of the 1000 m buffer in the simulated absence of human activities for the two road sections were significantly higher than their presence. This research identified the optimal road section for crossing the NHG to link giant panda population groups and habitats in Wolong. These insights are significant for formulating conservation decisions and corridor plans and for promoting wildlife conservation in reserves amid high levels of human activity.
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Affiliation(s)
- Hu Zhang
- Key Laboratory of Southwest China Wildlife Resources Conservation (Ministry of Education)China West Normal UniversityNanchongChina
| | - Zongkun Shi
- Key Laboratory of Southwest China Wildlife Resources Conservation (Ministry of Education)China West Normal UniversityNanchongChina
| | - Bin Feng
- Key Laboratory of Southwest China Wildlife Resources Conservation (Ministry of Education)China West Normal UniversityNanchongChina
- School of Ecology and EnvironmentTibet UniversityLhasaChina
| | - Ying Liu
- Key Laboratory of Southwest China Wildlife Resources Conservation (Ministry of Education)China West Normal UniversityNanchongChina
| | - Zhuo Tang
- Wolong National Nature Reserve AdministrationWenchuanChina
| | - Xin Dong
- Key Laboratory of Southwest China Wildlife Resources Conservation (Ministry of Education)China West Normal UniversityNanchongChina
- College of Environmental Science and EngineeringChina West Normal UniversityNanchongChina
| | - Xiaodong Gu
- Forestry and Grassland Administration of Sichuan Province & Sichuan Giant Panda National Park AdministrationChengduChina
| | - Dunwu Qi
- Chengdu Research Base of Giant Panda BreedingChengduChina
| | - Weihua Xu
- Research Center for Eco‐Environmental Sciences, Chinese Academy of SciencesBeijingChina
| | - Caiquan Zhou
- Key Laboratory of Southwest China Wildlife Resources Conservation (Ministry of Education)China West Normal UniversityNanchongChina
| | - Jindong Zhang
- Key Laboratory of Southwest China Wildlife Resources Conservation (Ministry of Education)China West Normal UniversityNanchongChina
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Zhao T, Miao C, Wang J, Su P, Chu K, Luo Y, Sun Q, Yao Y, Song Y, Bu N. Relative contributions of natural and anthropogenic factors to the distribution patterns of nature reserves in mainland China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 847:157449. [PMID: 35863564 DOI: 10.1016/j.scitotenv.2022.157449] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 07/13/2022] [Accepted: 07/13/2022] [Indexed: 06/15/2023]
Abstract
Nature reserves (NRs) are designated as a result of the ecosystem, species, economy, population, and land use coordination. However, the extent to which these factors influence the geographical pattern of NRs is unclear. Here, 11 indices (seven natural and four anthropogenic) were examined to identify these relationships in over 2600 terrestrial NRs in mainland China at the provincial level. Correlation analysis between natural and anthropogenic factors and NRs showed that desert and grassland had a positive correlation with NR coverage and area, and a negative correlation with NR density. This result was reversed in the correlation analysis between forest wetland coverage, endangered species, wildlife and NR coverage, area, and density. Similar results were found in the correlation analysis of all anthropogenic factors (population density, agricultural land, roads, and per capita GDP) with the coverage, area, and density of NRs. Canonical correspondence analysis (CCA) showed that three significant natural indicators (desert ecosystems, grasslands ecosystems, and forested and wetlands ecosystems) could explain 64.2 % of the pattern of NRs. The largest contributor was desert coverage, explaining 48.3 % (P = 0.002) of all indicators, followed by grassland coverage, explaining 8.6 % (P = 0.012), and forest and wetland coverage, explaining 7.3 % (P = 0.008). Human activities were significantly positively correlated with forest and wetland coverage, flora, and fauna, and negatively correlated with desert and grassland coverage. Compared with sand and grassland in the western region, the forest wetlands and wildlife in the eastern and central provinces were under greater pressure from anthropogenic activities. Therefore, natural factors determine the general layout of NRs, while the influence of anthropogenic activities makes the distribution of NRs patchy. When establishing national parks, governments must design strategies to coordinate areas with high biodiversity and high levels of human activity.
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Affiliation(s)
- Ting Zhao
- School of Environmental Science, Liaoning University, Shenyang 110036, China
| | - Congke Miao
- School of Environmental Science, Liaoning University, Shenyang 110036, China
| | - Jing Wang
- School of Environmental Science, Liaoning University, Shenyang 110036, China
| | - Pinjie Su
- School of Environmental Science, Liaoning University, Shenyang 110036, China
| | - Kuo Chu
- School of Environmental Science, Liaoning University, Shenyang 110036, China; Institute for Carbon Neutrality, Liaoning University, China
| | - Yifu Luo
- School of Environmental Science, Liaoning University, Shenyang 110036, China
| | - Qiqi Sun
- School of Environmental Science, Liaoning University, Shenyang 110036, China
| | - Yanzhong Yao
- School of Environmental Science, Liaoning University, Shenyang 110036, China
| | - Youtao Song
- School of Environmental Science, Liaoning University, Shenyang 110036, China
| | - Naishun Bu
- School of Environmental Science, Liaoning University, Shenyang 110036, China; Institute for Carbon Neutrality, Liaoning University, China; Key Laboratory of Wetland Ecology and Environment Research in Cold Regions of Heilongjiang Province, Harbin University, 150086, China; State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, 73 Huanghe Road, Harbin 150090, China.
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Balancing the Conservation and Poverty Eradication: Differences in the Spatial Distribution Characteristics of Protected Areas between Poor and Non-Poor Counties in China. SUSTAINABILITY 2022. [DOI: 10.3390/su14094984] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Understanding the spatial distribution characteristics of protected areas is the basis to balance the conservation and regional development. With the increasing number and area of protected areas, China has also made decisive progress in the fight against poverty. However, the spatial distribution characteristics of various types of protected areas in poor counties in China are still unclear and lacking further analysis on the differences compared to non-poor counties. Here, we first integrated the spatial distribution data of 8133 protected areas in China and overlaid them with 832 poor counties. Then we explored the spatial distribution characteristics of protected areas and the relationship with socio-economic and natural environment in poor and non-poor counties. The results showed that the area coverage of nature reserves in poor counties in China was significantly higher than that in non-poor counties (p < 0.001), while the area coverage of natural parks in non-poor counties was significantly higher than that in poor counties (p < 0.05). The area coverages of protected areas in poor counties in Northeast (p < 0.05), Southwest (p < 0.001), Central (p < 0.05), and East China (p < 0.01) were significantly higher than that in non-poor counties. Furthermore, the area coverage of nature reserves in poor counties was significantly positively correlated with mean elevation (p < 0.001), and the area coverage of natural parks in non-poor counties was significantly positively correlated with road network density (p < 0.05) and negatively correlated with the proportion of farmland (p < 0.001). This study can provide a reference to help China and other similar countries in the establishment of protected area systems to balance the conservation and poverty eradication for regional sustainable development.
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Shen J, Song Z, Duan W, Zhang Y. Exploring local challenges and adaptation strategies in the establishment of National Parks in giant panda habitats. Glob Ecol Conserv 2021. [DOI: 10.1016/j.gecco.2021.e01764] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
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The carrying pressure of livestock is higher than that of large wild herbivores in Yellow River source area, China. Ecol Modell 2020. [DOI: 10.1016/j.ecolmodel.2020.109163] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Giant Panda National Park, a step towards streamlining protected areas and cohesive conservation management in China. Glob Ecol Conserv 2020. [DOI: 10.1016/j.gecco.2020.e00947] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
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Shafer CL. Arguments for and against IUCN protected area management category VI with a review of state versus community governance. J Nat Conserv 2020. [DOI: 10.1016/j.jnc.2019.02.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Identifying Nature–Community Nexuses for Sustainably Managing Social and Ecological Systems: A Case Study of the Qianjiangyuan National Park Pilot Area. SUSTAINABILITY 2019. [DOI: 10.3390/su11216182] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Designing policies for the sustainable development of social-ecological systems with complex human–land relations requires integrated management and nexus thinking; China’s national parks are typical social-ecological systems. Ecosystem services and community livelihood are two essential components of sustainable management in the nature–community nexus (NCN). This study focuses on the Qianjiangyuan National Park Pilot Area in eastern China. Following a systems approach and integrating qualitative (causal analysis and systems but dynamic methods) and quantitative (InVEST model, Spearman’s correlation analysis, regression analysis, and multiple correspondence analysis) methods, we developed two causal mechanisms linking livelihood assets and ecosystem services, and verified them by exploring multi-dimensional linkages and revealing two types of NCNs. Results showed that the proportions of cropland and orchard areas have significant negative correlations with water and soil retention services, respectively, while forests significantly benefit both services. A positive NCN exists in areas where water and soil retention services perform well and the local community develops vibrantly with a considerable proportion of young, highly educated, or high-income (especially the income from secondary industries) residents. A negative NCN is seen in areas where the water and soil retention services values are low; a great many households do not have substantial income from secondary and tertiary industries, and few households have vast forest areas. These results can be used as scientific evidence for optimizing institutional arrangements and contributing to sustainable and harmonious development of national parks in China.
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